Novel Bayesian Method to Derive Final Adjusted Values of Physicochemical Properties: Application to 74 Compounds

Accurate values of physicochemical properties are essential for screening semivolatile organic compounds for human and environmental hazard and risk. In silico approaches for estimation are widely used, but the accuracy of these and measured values can be difficult to ascertain. Final adjusted values (FAVs) harmonize literature-reported measurements to ensure consistency and minimize uncertainty. We propose a workflow, including a novel Bayesian approach, for estimating FAVs that combines measurements using direct and indirect methods and in silico values. The workflow was applied to 74 compounds across nine classes to generate recommended FAVs (FAV^Rs). Estimates generated by in silico methods (OPERA, COSMOtherm, EPI Suite, SPARC, and polyparameter linear free energy relationships (pp-LFER) models) differed by orders of magnitude for some properties and compounds and performed systematically worse for larger, more polar compounds. COSMOtherm and OPERA generally performed well with low bias although no single in silico method performed best across all compound classes and properties. Indirect measurement methods produced highly accurate and precise estimates compared with direct measurement methods. Our Bayesian method harmonized measured and in silico estimated physicochemical properties without introducing observable biases. We thus recommend use of the FAV^Rs presented here and that the proposed Bayesian workflow be used to generate FAV^Rs for SVOCs beyond those in this study.